Forecasting Long-term Electricity Demand in the Residential Sector

Pessanha, José Francisco Moreira; León, Nelson

doi:10.1016/j.procs.2015.07.032

Cited by 21 publications

(10 citation statements)

References 4 publications

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“…Few research aricles has considered sectoral energy demand forecasting. For example, in forecasting long-term electricity demand for the residential sector, Pessanha [42] decomposed the total electricity residential consumption into three components, namely: average consumption per consumer unit, electrification rate, and the number of households and forecasted total electricity consumption in the residential sector by finding the product of the three components. The proposed methodology provided a framework to integrate the macroeconomic scenario, demographic projection, and assumptions for ownership and efficiency of electric appliances in a ten (10) year demand forecast for Brazil.…”

Section: Discussionmentioning

confidence: 99%

Sectoral Energy Demand Forecasting under an Assumption-Free Data-Driven Technique

Ameyaw

Yao

2018

Sustainability

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In order to implement sustainable economic policies, realistic and high accuracy demand projections are key to drawing and implementing realizable environmentally-friendly energy policies. However, some core energy models projections depict considerably high forecast inaccuracies in their previous projections. The inaccuracies are due to the massive assumption-driven variables whose assumptions and scenarios typically deviate from their realized levels. Here, we propose a high-accuracy assumption-free own-data-driven technique that utilizes zero of the traditional determinants as well as assumptions or scenarios for sectorial energy demand forecasting; and implement it in the United States (U.S.). The results show that the forecast accuracy of our gated recurrent network presents an enormous improvement on Annual Energy Outlook 2008 forecast projections. With evidence that our proposed sequential algorithm outperformed Annual Energy Outlook 2008 forecast projections, our proposed algorithm will guide policymakers in making sustainable energy-related policies in the near future. Although future realized consumption levels are unknown, we present our estimated projections along with Annual Energy Outlook 2018 projections to inform policymakers on future energy demands for the commercial sector, industrial sector, residential sector, and transportation.

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Section: Discussionmentioning

confidence: 99%

Sectoral Energy Demand Forecasting under an Assumption-Free Data-Driven Technique

Ameyaw

Yao

2018

Sustainability

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“…This process is even more intense in the Brazilian rural areas, due to the urbanization phenomenon association. With the interest to refine the understanding of residential electricity consumption growth, in recent years, the literature has been introducing population age and demographic processes in the analyses (BARDAZZI;PAZIENZIA, 2020;ESTIRI;ZAGHENI, 2019;LIDDLE, 2013;DEUTSCH;TIMPE, 2013;LEON, 2012LEON, , 2015BROUNEN;KOK;QUIGLEY, 2012;TONN;EISENBERG, 2006;O'NEIL;CHEN, 2002).…”

Section: Introductionmentioning

confidence: 99%

“…This growth will require an increase in the number of households, and an expansion in the capacity of electricity generation to meet the energy consumption growth. In addition to the expansion of population and household numbers, other factors such as family income, electricity tariff, economic growth, climate, increase of appliance ownership, penetration of new appliances and technologies that offer personal and home services, use intensity of equipment, connectivity by internet, and a change in consumer preferences may also contribute to the growth of residential electricity consumption (ABRAHÃO; SOUZA, [2021]; VILLAREAL; MOREIRA, LEON, 2015;ACHÃO;SCHAEFFER, 2009;GOSCH;LAMBERTS, 2007;ALMEIDA;LA ROVERE, 2001). On the other hand, energy efficiency policies help to slow electricity consumption growth (MORISHITA; GHISI, 2010).…”

Section: Introductionmentioning

confidence: 99%

What has driven the growth of Brazil´s residential electricity consumption during the last 19 years? An index decomposition analysis

Abrahão

Souza

2021

Ambient. constr.

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Residential electricity consumption in Brazil has been growing during the last few decades, creating a potential opportunity to expand energy efficiency measures. However, the dimension of the sector and its closed relationship with the economic, cultural, and demographic processes causes a certain complexity in the understanding of patterns of consumption, creating additional challenges to energy policies. This study analyzed and decomposed the Brazilian residential electricity consumption between the years of 2000 and 2018, by driving factors through the LMDI-I method and IDA index, on regional level. All the data were obtained by official sources in the country. The main results obtained were: (i) the increase of household numbers was found to be one of the main drivers of consumption growth; (ii) household income showed no control over consumption in hot climate regions, except in low- income households; (iii) tariff showed to impose restrictions on consumption, also mainly in low-income households. Unprecedentedly, the results showed that the electricity consumption in Brazil varies with population age, with a trend of consumption growth up to the age 59, and a sharp reduction from the age of 60. The study presents opportunities to be contemplated in research and in energy policies.

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“…Forecasting electricity demand is vital for power generation and planning. Accurate forecast of electricity demands presents a better understanding of the electricity network expansion and generation to sustainably cater for future demands [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Time Series Prediction of Electricity Demand Using Adaptive Neuro-Fuzzy Inference Systems

Acakpovi

Ternor

Asabere

et al. 2020

Mathematical Problems in Engineering

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This paper is concerned with the reliable prediction of electricity demands using the Adaptive Neuro-Fuzzy Inference System (ANFIS). The need for electricity demand prediction is fundamental and vital for power resource planning and monitoring. A dataset of electricity demands covering the period of 2003 to 2018 was collected from the Electricity Distribution Company of Ghana, covering three urban areas namely Mallam, Achimota, and Ga East, all in Ghana. The dataset was divided into two parts: one part covering a period of 0 to 500 hours was used for training of the ANFIS algorithm while the second part was used for validation. Three scenarios were considered for the simulation exercise that was done with the MATLAB software. Scenario one considered four inputs sampled data, scenario two considered an additional input making it 5, and scenario 3 was similar to scenario 1 with the exception of the number of membership functions that increased from 2 to 3. The performance of the ANFIS algorithm was assessed by comparing its predictions with other three forecast models namely Support Vector Regression (SVR), Least Square Support Vector Machine (LS-SVM), and Auto-Regressive Integrated Moving Average (ARIMA). Findings revealed that the ANFIS algorithm can perform the prediction accurately, the ANFIS algorithm converges faster with an increase in the data used for training, and increasing the membership function resulted in overfitting of data which adversely affected the RMSE values. Comparison of the ANFIS results to other previously used methods of predicting electricity demands including SVR, LS-SVM, and ARIMA revealed that there is merit to the potentials of the ANFIS algorithm for improved predictive accuracy while relying on a quality data for training and reliable setting of tuning parameters.

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Forecasting Long-term Electricity Demand in the Residential Sector

Cited by 21 publications

References 4 publications

Sectoral Energy Demand Forecasting under an Assumption-Free Data-Driven Technique

Sectoral Energy Demand Forecasting under an Assumption-Free Data-Driven Technique

What has driven the growth of Brazil´s residential electricity consumption during the last 19 years? An index decomposition analysis

Time Series Prediction of Electricity Demand Using Adaptive Neuro-Fuzzy Inference Systems

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